What's the best paper researching seed numbers in generative ai?
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Seed numbers in generative AI have been researched in several papers. Chirila et al. propose a new interactive model of generative learning objects that use random numbers for automatic initialization, increasing content diversity and learner engagement . Zammit et al. explore the combination of novelty preservation mechanisms in evolutionary algorithms with generative adversarial networks (GANs) to guide the creative process towards novel outputs . Lahtinen presents a method for computing the seed number used in encryption algorithms, which is generated from a random number provided by the authentication center of a mobile communication network . Jang demonstrates the generation of realistic chest X-ray images using a GAN model with true-random numbers generated from a memristor array . While all these papers touch upon seed numbers in generative AI, the best paper specifically researching seed numbers in this context is not explicitly mentioned in the provided abstracts.
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| Papers (5) | Insight |
|---|---|
| The provided paper does not discuss the best paper researching seed numbers in generative AI. It focuses on generating realistic chest X-ray images using a generative adversarial network (GAN) model with true-random numbers (TRNs) generated from a memristor array. | |
| The provided paper does not research seed numbers in generative AI. It is about a method for computing the key to an encryption algorithm used in a telecommunication network. | |
| The provided paper does not specifically research seed numbers in generative AI. It focuses on combining evolutionary algorithms with GANs to guide the creative process towards more novel outputs. | |
Open access 17 Dec 2015 | The provided paper does not specifically research seed numbers in generative AI. It focuses on the development of generative learning objects using random numbers for increased interactivity and engagement in e-learning. |
| The provided paper does not specifically research seed numbers in generative AI. It focuses on combining evolutionary algorithms with GANs to guide the creative process towards more novel outputs. |
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